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Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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11#
發(fā)表于 2025-3-23 12:13:30 | 只看該作者
,Real-Time Neural Character Rendering with?Pose-Guided Multiplane Images,portable camera rig to capture the multi-view images along with the driving signal for the moving subject. Our method generalizes the image-to-image translation paradigm, which translates the human pose to a 3D scene representation—MPIs that can be rendered in free viewpoints, using the multi-views
12#
發(fā)表于 2025-3-23 14:34:47 | 只看該作者
13#
發(fā)表于 2025-3-23 20:17:32 | 只看該作者
Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth,ynamic scenes due to the mismatch and occlusion problems introduced by object motions. Existing dynamic-object-focused methods only partially solved the mismatch problem at the training loss level. In this paper, we accordingly propose a novel multi-frame monocular depth prediction method to solve t
14#
發(fā)表于 2025-3-24 00:46:59 | 只看該作者
15#
發(fā)表于 2025-3-24 04:39:38 | 只看該作者
Context-Enhanced Stereo Transformer,azardous regions, such as large uniform regions. To overcome these limitations, we propose Context Enhanced Path (CEP). CEP improves the generalization and robustness against common failure cases in existing solutions by capturing the long-range global information. We construct our stereo depth esti
16#
發(fā)表于 2025-3-24 10:02:21 | 只看該作者
PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matching,on for other datasets or focus on handling the cross-domain generalization by suppressing the domain sensitive features which results in a significant sacrifice on the performance. To tackle these problems, we propose PCW-Net, a .yramid .ombination and .arping cost volume-based network to achieve go
17#
發(fā)表于 2025-3-24 14:37:45 | 只看該作者
,Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from?RGB Images, high-quality object models or require additional depth maps or object masks in test time, which significantly limits their application scope. In contrast, our pose estimator only requires some posed images of the unseen object and is able to accurately predict poses of the object in arbitrary envir
18#
發(fā)表于 2025-3-24 16:28:34 | 只看該作者
Latency-Aware Collaborative Perception,rception methods usually consider an ideal communication environment. However, in practice, the communication system inevitably suffers from latency issues, causing potential performance degradation and high risks in safety-critical applications, such as autonomous driving. To mitigate the effect ca
19#
發(fā)表于 2025-3-24 22:02:55 | 只看該作者
20#
發(fā)表于 2025-3-24 23:19:26 | 只看該作者
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